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vegetation and population density in urban and suburban areas in the u s a n.
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Vegetation and Population Density in Urban and Suburban Areas in the U.S.A. PowerPoint Presentation
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Vegetation and Population Density in Urban and Suburban Areas in the U.S.A.

Vegetation and Population Density in Urban and Suburban Areas in the U.S.A.

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Vegetation and Population Density in Urban and Suburban Areas in the U.S.A.

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  1. Vegetation and Population Density in Urban and Suburban Areas in the U.S.A. Francesca Pozzi Center for International Earth Science Information Network Columbia University New York, USA Christopher Small Lamont-Doherty Earth Observatory Columbia University New York, USA Istanbul, 11-13 June 2002

  2. Objectives • Characterize urban areas based on demographic and physical characteristics: • Population Density • Vegetation Abundance • Examine consistency of relationship between the two variables in the USA • Compare with existing land cover classification (USGS) • Can this help us find alternative classification systems for urban areas?

  3. Case Study: The USA • 6 cities with different geographic location, physical environment and urban growth dynamics Atlanta Chicago Los Angeles New York Phoenix Seattle

  4. Data: Population Density • 1990 US Census Bureau population counts at the block level (Spatial and tabular data) • Density in people/km2 • Data reprojected to UTM, • Rasterized to 30 m, • Co-registred to Landsat New York City

  5. Data: Vegetation Abundance • Landsat TM data, circa 1990 • Spectral reflectance of many urban areas can be described as linear mixing of: • Low albedo • High albedo • Vegetation • Linear un-mixing • Fraction images showing areal % of each endmember within each pixel (0 to 1) • Validation with IKONOS, accuracy within 10% Vegetation Fraction (White = 0, Dark Green = 1)

  6. Data: USGS National Land Cover Dataset • Based on Landsat TM data • Nominal-1992 acquisitions • Modified Anderson LULC Classification System • Selected 3 “Developed” classes: • Low Intensity Residential • High Intensity Residential • Commercial/Industrial/Transportation USGS NLCD “Developed” classes (Light orange = LIR, Orange = HIR, Red = CIT)

  7. Analysis • Analysis of population distributions across the entire U.S. • Demographic Classification • Quantification of the relationship between population density and vegetation fraction • Bivariate distributions • Marginal distributions • Comparison with USGS NLCD Classes • Distributions of areal extent of each USGS class as a function of population density and vegetation fraction

  8. Population Density Distribution in the U.S. • Multimodal Distribution • Modes are: • Rural: pop. dens. <100 • Suburban: 100 <pop.dens. < 10,000 • Urban: pop. dens >10,000 people/km2 • Grey line: Western US (west of the 90 ° W) • Black line: Eastern US

  9. Geographic Distribution of U.S. Population

  10. Demographic Classification Population Density Example: portion of Chicago 3 Classes of population density  Demographic Classification Overlay with vegetation fraction Blue: Rural Green: Suburban Red: Urban Demographic Classification Vegetation Fraction

  11. Bivariate Distributions Distributions of people as functions of Population Density and Vegetation Fraction Legend: warmer colors = higher numbers of people on Log scale

  12. Marginal Distributions

  13. Comparison with USGS NLCD Classes • Distributions of areal extent of each USGS “Developed” class as functions of population density and vegetation fraction • Red: High Intensity Residential • Green: Low Intensity Residential • Blue: Commercial/Industrial/Transportation

  14. Comparison with USGS NLCD Classes • Visual comparison between Demographic Classification and USGS NLCD “Developed” Classes • Legend: • Blue: Rural/CIT • Green: Suburban/LIR • Red: Urban/HIR • Cities: • Top: Chicago • Middle: New York • Bottom: Los Angeles

  15. Conclusions • Population density distribution in the U.S. demographic classification (urban/suburban/rural) • Vegetation cover is the most consistent spectral characteristics in suburban areas • Spectral heterogeneity  wide range of vegetation fractions in demographically urban and suburban areas • Not possible to consistently characterize urban and suburban areas in the U.S. based on reflectance characteristics at Landsat resolutions

  16. What next? • Emphasize results on quantitative characterization of vegetation abundance as means to provide physical basis for comparison of urban environments • Explore classification schemes based on spectral heterogeneity at multiple pixel scales, supplemented by auxiliary data sources • Demographic Classification for the year 2000 and urban sprawl analysis

  17. Thank you! fpozzi@ciesin.columbia.edu http://sedac.ciesin.columbia.edu/urban_rs